Image-based Computational Fluid Dynamics: A New Paradigm for Monitoring Hemodynamics and Atherosclerosis
Bibliographic record
Abstract
Complex blood flow dynamics are thought to play a key role in the development and treatment of atherosclerosis; however, the exact nature of this role is incompletely understood owing to the practical difficulties associated with measuring important local hemodynamic factors, notably wall shear stresses, in vivo. Only recently has it become possible to consider mapping these hemodynamic factors in a prospective, patient-specific manner via the coupling of in vivo medical imaging and computational fluid dynamics (CFD) modelling. CFD models derived from intravascular ultrasound have already been used to elucidate the role that hemodynamic forces play in mechanical and pharmacological interventions for coronary atherosclerosis. CFD models derived from magnetic resonance imaging and three-dimensional ultrasound provide a less invasive window into more superficial vessels such as the carotid bifurcation, and thus are promising tools for clarifying the role of, and eventually exploiting, purported local geometric and hemodynamic risk factors for atherosclerosis and its response to therapeutic options. Efforts to improve the ease and robustness with which these models are constructed have led to concomitant improvements in accuracy and precision, data for which are presented to facilitate estimation of sample sizes for future studies. Current limitations and anticipated future directions for these powerful new tools are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".